# Copyright    2021  Xiaomi Corp.        (authors: Fangjun Kuang)
#
# See ../../../../LICENSE for clarification regarding multiple authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
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import torch
import torch.nn as nn
import torch.nn.functional as F


class Decoder(nn.Module):
    """This class modifies the stateless decoder from the following paper:

        RNN-transducer with stateless prediction network
        https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9054419

    It removes the recurrent connection from the decoder, i.e., the prediction
    network. Different from the above paper, it adds an extra Conv1d
    right after the embedding layer.

    TODO: Implement https://arxiv.org/pdf/2109.07513.pdf
    """

    def __init__(
        self,
        vocab_size: int,
        decoder_dim: int,
        blank_id: int,
        context_size: int,
    ):
        """
        Args:
          vocab_size:
            Number of tokens of the modeling unit including blank.
          decoder_dim:
            Dimension of the input embedding, and of the decoder output.
          blank_id:
            The ID of the blank symbol.
          context_size:
            Number of previous words to use to predict the next word.
            1 means bigram; 2 means trigram. n means (n+1)-gram.
        """
        super().__init__()

        self.embedding = nn.Embedding(
            num_embeddings=vocab_size,
            embedding_dim=decoder_dim,
        )
        self.blank_id = blank_id

        assert context_size == 1, context_size
        self.context_size = context_size
        self.vocab_size = vocab_size

    def forward(self, y: torch.Tensor, need_pad: bool = True) -> torch.Tensor:
        """
        Args:
          y:
            A 2-D tensor of shape (N, U).
          need_pad:
            True to left pad the input. Should be True during training.
            False to not pad the input. Should be False during inference.
        Returns:
          Return a tensor of shape (N, U, decoder_dim).
        """
        y = y.to(torch.int64)
        # this stuff about clamp() is a temporary fix for a mismatch
        # at utterance start, we use negative ids in beam_search.py
        if torch.jit.is_tracing():
            # This is for exporting to PNNX via ONNX
            embedding_out = self.embedding(y)
        else:
            embedding_out = self.embedding(y.clamp(min=0)) * (y >= 0).unsqueeze(-1)

        embedding_out = F.relu(embedding_out)
        return embedding_out